Neural network-based state observation utilizing a history-of-error performance index
Bibliographic record
Abstract
Accurate state estimation is crucial for the control and monitoring of multivariable nonlinear systems. Neural network-based observers offer promising solutions due to their universal approximation capabilities; however, maintaining precision and robustness in the presence of nonlinearities and parametric uncertainties remains a significant challenge. This paper presents an adaptive neural network observer that incorporates a history-of-error term into the weight update rules of a modified backpropagation algorithm. An e-modification term is introduced to ensure bounded state-estimation errors, with stability formally established through a Lyapunov-based analysis. Simulation and experimental studies on a one-link arm under gravity, actuated by a DC motor, demonstrate that the proposed observer can significantly enhance the estimation accuracy and convergence speed when compared to conventional neural network observers. Comparative studies indicate an approximate 50% improvement in state estimation and control accuracy, highlighting the effectiveness of the proposed approach. • Adaptive NN observer with error-history index improves accuracy, speed, convergence. • Backpropagation with e-modification guarantees bounded state estimation errors. • Stability and convergence ensured via Lyapunov-based theoretical analysis. • Achieves more than 50% improvement in state estimation vs. conventional NN observers. • Experimental evaluation confirm improved convergence and tracking performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".